Two apparent 'counterexamples' to Marcus: A closer look [Book Review]
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Minds and Machines 15 (3-4):359-382 (2005)
Marcus et al.’s experiment (1999) concerning infant ability to distinguish between differing syntactic structures has prompted connectionists to strive to show that certain types of neural networks can mimic the infants’ results. In this paper we take a closer look at two such attempts: Shultz and Bale [Shultz, T.R. and Bale, A.C. (2001), Infancy 2, pp. 501–536] Altmann and Dienes [Altmann, G.T.M. and Dienes, Z. (1999) Science 248, p. 875a]. We were not only interested in how well these two models matched the infants’ results, but also whether they were genuinely learning the grammars involved in this process. After performing an extensive set of experiments, we found that, at first blush, Shultz and Bale’s model (2001) replicated the infant’s known data, but the model largely failed to learn the grammars. We also found serious problems with Altmann and Dienes’ model (1999), which fell short of matching any of the infant’s results and of learning the syntactic structure of the input patterns
|Keywords||artificial grammars connectionism grammar learning neural networks simple recurrent networks syntactic structures|
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References found in this work BETA
Zoltán Dienes, Gerry T. M. Altmann & Shi‐Ji Gao (1999). Mapping Across Domains Without Feedback: A Neural Network Model of Transfer of Implicit Knowledge. Cognitive Science 23 (1):53-82.
Robert F. Hadley (1995). The 'Explicit-Implicit' Distinction. Minds and Machines 5 (2):219-42.
Citations of this work BETA
Thomas R. Shultz & Alan C. Bale (2006). Neural Networks Discover a Near-Identity Relation to Distinguish Simple Syntactic Forms. Minds and Machines 16 (2):107-139.
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